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Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes. Data and methods may be requested from an author years after publication. In order to encourage data sharing [3] and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data ...
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
In some domains, a few dozen different source and target schema (proprietary data formats) may exist. An "exchange" or "interchange format" is often developed for a single domain, and then necessary routines (mappings) are written to (indirectly) transform/translate each and every source schema to each and every target schema by using the interchange format as an intermediate step.
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
Knowledge sharing (fostering a culture that encourages the sharing of information, based on the concept that knowledge is not irrevocable and should be shared and updated to remain relevant) Make knowledge-sharing a key role in employees' job description; Inter-project knowledge transfer; Intra-organisational knowledge sharing
Information exchange or information sharing means that people or other entities pass information from one to another. This could be done electronically or through certain systems. [ 1 ] These are terms that can either refer to bidirectional information transfer in telecommunications and computer science or communication seen from a system ...
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Data modeling during systems analysis: In systems analysis logical data models are created as part of the development of new databases. Data modeling is also used as a technique for detailing business requirements for specific databases. It is sometimes called database modeling because a data model is eventually implemented in a database. [4]